scholarly journals Measuring on-Road Vehicle Hot Running NOx Emissions with a Combined Remote Sensing–Dynamometer Study

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 294 ◽  
Author(s):  
Robin Smit ◽  
Daniel Kennedy
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Time Resolution ◽  
Bias Correction ◽  
Nox Emissions ◽  
Positive Bias ◽  
Drive Cycle ◽  
Regional Emissions ◽  
High Level ◽  
Weighted Correlation

This study explores the correlation in measured hot running NO/CO2 ratios by a remote sensing device (RSD) and dynamometer testing. Two large diesel cars (E4/E5) are tested on the dynamometer in hot running conditions using a new drive cycle developed for this study and then driven multiple times past the RSD. A number of verification and correction steps are conducted for both the dynamometer and RSD data. A new time resolution adjustment of RSD acceleration values proves important. Comparison of RSD and dynamometer data consistently shows a strong weighted correlation varying from +0.89 to +0.95, despite the high level of variability observed in the RSD measurements. This provides further evidence that relative changes in mean NO/CO2 ratios as measured with the RSD should provide robust emissions data for trend analysis studies and as inputs for regional emissions models. However, a positive bias of approximately 25 ppm NO/% CO2 is observed for the RSD, and bias correction of RSD measurements should be considered pending further testing.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

Methodology to Evaluate the Fuel Economy of a Multimode Combustion Engine With Three-Way Catalytic Converter

2014 ◽  
Author(s):  
Sandro P. Nüesch ◽  
Anna G. Stefanopoulou ◽  
Li Jiang ◽  
Jeffrey Sterniak
Keyword(s):  
Fuel Economy ◽  
Combustion Engine ◽  
Catalytic Converter ◽  
Oxygen Storage ◽  
Nox Emissions ◽  
Combustion Mode ◽  
Mode Switch ◽  
Drive Cycle ◽  
Finite State ◽  
The Impact

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultra-low levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper a methodology is introduced, investigating the fuel economy of such a multimode combustion concept in combination with a three-way catalytic converter (TWC). The TWC needs to exhibit unoccupied oxygen storage sites in order to show acceptable performance. But the lean exhaust gas during HCCI operation fills the oxygen storage and leads to a drop in NOx conversion efficiency. Eventually the levels of NOx become unacceptable and a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage. The resulting lean-rich cycling leads to a penalty in fuel economy. In order to evaluate the impact of those penalties on fuel economy, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI and back. Fuel and emissions maps acquired in steady state experiments are used. Two depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions.

Bridge-based remote sensing of NOx emissions from locomotives

2019 ◽  
Vol 198 ◽  
pp. 77-82
Author(s):  
Matthew A. Breuer ◽  
Daniel A. Burgard
Keyword(s):  
Remote Sensing ◽  
Nox Emissions

scholarly journals Influence of blends of diesel and renewable fuels on CI engine emissions over transient engine conditions.

2018 ◽  
Author(s):  
Adriaan Smuts Van Niekerk ◽  
Benjamin Drew ◽  
Neil Larsen ◽  
Peter Kay
Keyword(s):  
Fuel Consumption ◽  
Co2 Emissions ◽  
Nox Emissions ◽  
Compression Ignition ◽  
Engine Emissions ◽  
Drive Cycle ◽  
Fuel Blend ◽  
High Oxygen Content ◽  
Fuel Blends ◽  
Ci Engine

To reduce the amount of carbon dioxide released from transportation the EU has implemented legislation to mandate the renewable content of petrol and diesel fuels. However, due to the complexity of the combustion process the addition of renewable content, such as biodiesel and ethanol, can have a detrimental effect on other engine emissions. In particular the engine load can have a significant impact on the emissions. Most research that have studied this issue are based on steady state tests, that are unrealistic of real world driving and will not capture the difference between full and part loads. This study aims to address this by investigating the effect of renewable fuel blends of diesel, biodiesel and ethanol on the emissions of a compression ignition engine tested over the World Harmonised Light Vehicle Test Procedure (WLTP). Diesel, biodiesel and ethanol were blended to form binary and ternary blends, the ratios were determined by Design of Experiments (DoE). The total amount of emissions for CO, CO2 and NOx as well as the fuel consumption, were measured from a 2.4 liter compression ignition (CI) engine running over the WLTP drive cycle. The results depicted that percentages smaller than 10 % of ethanol in the fuel blend can reduce CO emissions, CO2 emissions as well as NOx emissions, but increases fuel consumption with increasing percentage of ethanol in the fuel blend. Blends with biodiesel resulted in minor increases in CO emissions due to the engine being operated in the low and medium load regions over the WLTP. CO2 emissions as well as NOx emissions increased as a result of the high oxygen content in biodiesel which promoted better combustion. Fuel consumption increased for blends with biodiesel as a result from biodiesel's lower heating value. All the statistical models describing the engine responses were significant and this demonstrated that a mixture DoE is suitable to quantify the effect of fuel blends on an engine's emissions response. An optimised ternary blend of B2E9 was found to be suitable as a 'drop in' fuel that will reduce harmful emissions of CO emissions by approximately 34 %, NOx emissions by 10 % and CO2 emissions by 21 % for transient engine operating scenarios such as the WLTP drive cycle.

scholarly journals A spatial and temporal analysis on NDVI in Urad grassland during 2010-2019 over remote sensing

2021 ◽  
Vol 336 ◽  
pp. 06029
Author(s):  
Yueying Zhang ◽  
Tiantian Liu ◽  
Yuxi Wang ◽  
Ming Zhang ◽  
Yu Zheng
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Temporal Analysis ◽  
Vegetation Coverage ◽  
Analysis Method ◽  
Modis Ndvi ◽  
The Past ◽  
Spatial And Temporal Analysis ◽  
Change Trend ◽  
Temporal And Spatial

The temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland, Inner Mongolia has been investigated by analysing on MODIS NDVI remote sensing products. This paper applies pixel dichotomy approach and linear regression trend analysis method to analyze the temporal and spatial evolution trend of vegetation coverage over the past 10 years. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. The vegetation distribution and change trend analysis provide a thorough and scientific reference for policymaking in environmental protection.

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